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Introduction Version

T-test with indep. samples result

Typing name :  TASK.gws_stats.TTestTwoIndepSamples Brick :  gws_stats v

Test that the means of two independent samples are equal. Performs pairwise analysis for more than two samples.

Compute the T-test for the means of independent samples, from a given reference sample.

This test is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values. Performs pairwise analysis for more than two samples.

  • Input: a table containing the sample measurements, with the name of the samples.
  • Output: a table listing the correlation coefficient, and its associated p-value for each pairwise comparison testing.
  • Config Parameters:
    • preselected_column_names: List of columns to pre-select for pairwise comparisons. By default a maximum pre-defined number of columns are selected (see configuration).
    • reference_column: If given, this reference column is compared against all the other columns.
    • row_tag_key: If give, this parameter is used for group-wise comparisons along row tags (see example below). This parameter is ignored of a reference_column is given.
    • adjust_pvalue:
      • method: The correction method for p-value adjustment in multiple testing.
      • alpha: The FWER, family-wise error rate. Default is 0.05.
    • "equal_variance": a boolean parameter setting whether populations have equal variance.
    • alternative_hypothesis: The alternative hypothesis chosen for the testing (two-sided, less or greater)

Example 1: Direct column comparisons

Let's say you have the following table.

1 5 3
2 6 8
3 7 5
4 8 4

This task performs pairwise comparison of almost all the columns of the table (the first 500 columns are pre-selected by default).

  • A will be compared with B and with C, respectively
  • B will be compared with C

To only compare a given column with all the others, set the name of the reference_column (a.k.a Reference column). Suppose that B is used as reference column,only the following comaprisons will be done:

  • B versus A
  • B versus C

It is also possible to perform comparison on a well-defined subset of the table by pre-selecting the columns of interest. Parameter preselected_column_names (a.k.a. Selected columns names) allows pre-selecting a subset of columns for analysis.

Example 2: Advanced comparisons along row tags using row_tag_key parameter

In general, the table rows represent real-world observations (e.g. measured samples) and columns correspond to descriptors (a.k.a features or variables). Theses rows (samples) may therefore be related to metadata information given by row tags as follows:

row_tags A B C
Gender : M
Age : 10
1 5 3
Gender : F
Age : 10
2 6 8
Gender : F
Age : 10
3 7 5
Gender : M
Age : 20
4 8 4

Actually, the column row_tags does not really exist in the table. It is just to show here the tags of the rows Here, the first row correspond to 10-years old male individuals. In this this case, we may be interested in only comparing each columns along row metadata tags. For instance, to compare Males (M) versus Females (F) of each columns separately, you can use the advance parameter row_tag_key=Gender.

For more details, see https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html


The input table


The output result




The names of column to pre-select for comparison. By default, the first 500 columns are used

Type : ListMaximum occurrences number : -1



The name of the column(s) to pre-select

Type : string



Set True if it is a text pattern (regular expression), False otherwise

Type : bool



The column used as reference for pairwise comparison. Only this column is compared with the others.

Type : string


OptionalAdvanced parameter

The key of the row tag (representing the group axis) along which one would like to compare each column. This parameter is not used if a `reference column` is given.

Type : string


OptionalAdvanced parameter

Adjust p-values for multiple tests.

Type : ListMaximum occurrences number : 1


OptionalAdvanced parameter

The method used to adjust (correct) p-values

Type : stringAllowed values : bonferroni  fdr_bh  fdr_by  fdr_tsbh  fdr_tsbky  sidak  holm-sidak  holm  simes-hochberg  hommel  Default value : bonferroni


OptionalAdvanced parameter

FWER, family-wise error rate. Default is 0.05

Type : floatDefault value : 0.05



Set True to assume that the populations have equal variance; False otherwise

Type : boolDefault value : true



The alternative hypothesis chosen for the testing.

Type : stringAllowed values : two-sided  less  greater  Default value : two-sided